collaborators

8 papers

cs.CL2026

Right Answers, Wrong States: Hidden Information Failures in Multi-Agent Collaboration

Herun Wan, Jiaying Wu, Minnan Luo +4

Multi-agent systems are often judged by whether they reach the correct answer. This can miss a distinct failure: collaboration may leave behind a corrupted information state even w…

cs.CL2026

The Deliberative Illusion: Diagnosing Factual Attrition and Stance Homogenization in Multi-Agent LLM Deliberation

Herun Wan, Jiaying Wu, Minnan Luo +4

Multi-agent LLM systems often treat consensus as evidence of successful interaction. For deliberative problems, however, reliability depends on whether agents preserve the facts an…

cs.CL2026

Better with Experience: Self-Evolving LLM Agents for Evidence-Grounded Health Community Notes

Zihang Fu, Fanxiao Li, Jianyang Gu +5

Large Language Model (LLM)-augmented Community Notes offer a scalable path for timely, evidence-grounded correction of health misinformation on social platforms. However, they stil…

cs.CR2026

FlowSteer: Prompt-Only Workflow Steering Exposes Planning-Time Vulnerabilities in Multi-Agent LLM Systems

Fanxiao Li, Jiaying Wu, Tingchao Fu +3

Multi-agent systems (MAS) powered by large language models (LLMs) increasingly adopt planner--executor architectures, where planners convert prompts into subtasks, roles, dependenc…

cs.CL2026

The Facade of Truth: Uncovering and Mitigating LLM Susceptibility to Deceptive Evidence

Herun Wan, Jiaying Wu, Minnan Luo +3

To reliably assist human decision-making, LLMs must maintain factual internal beliefs against misleading injections. While current models resist explicit misinformation, we uncover…

cs.CV2026

What's Left Unsaid? Detecting and Correcting Misleading Omissions in Multimodal News Previews

Fanxiao Li, Jiaying Wu, Tingchao Fu +4

Even when factually correct, social-media news previews (image-headline pairs) can induce interpretation drift: by selectively omitting crucial context, they lead readers to form j…

cs.SI2025

Forecasting the Buzz: Enriching Hashtag Popularity Prediction with LLM Reasoning

Yifei Xu, Jiaying Wu, Herun Wan +3

Hashtag trends ignite campaigns, shift public opinion, and steer millions of dollars in advertising spend, yet forecasting which tag goes viral is elusive. Classical regressors dig…

cs.SI2025

Beyond the Crowd: LLM-Augmented Community Notes for Governing Health Misinformation

Jiaying Wu, Zihang Fu, Haonan Wang +4

Community Notes, the crowd-sourced misinformation governance system on X (formerly Twitter), allows users to flag misleading posts, attach contextual notes, and rate the notes' hel…